case-studies on cfd simulation of windshield de-icing

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Case-Studies on CFD Simulation

of Windshield De-icing

Padmesh Mandloi1 and Nidhesh Jain

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Abstract

CFD Simulation of windshield de-icing has gained a lot of importance over the last few

years. With stricter regulatory norms, both OEMs and suppliers have to constantly work on

improving the defroster design [1], [2]. Experimental testing is extremely time-consuming as

well as quite expensive. CFD simulations have been able to shorten the development cycle

and reduce cost.

Windshield deicing simulations involve both geometrical complexities as well as complex

physics. It involves interaction between the airflow and two modes of heat transfer, basically

conduction and convection [1]. A variety of factors play a very important role in accurately

predicting the deicing process and deicing pattern. These factors include defroster angle with

the windshield, mesh size and mesh type near the windshield and defroster outlet, thermal

conductivity and specific heat considerations due to composite laminate windshield, effect of

the melting of ice due to deicing, turbulence modeling etc. Without considering these effects,

the deicing rates cannot be correctly predicted.

An attempt is made here to study the effect of the above-mentioned factors on deicing

patterns. A general purpose CFD solver, FLUENT 6.3 is used to simulate the deicing process.

The melting of ice is simulated using phase change model which is based on the enthalpy-

porosity techniques. Simplified cabin geometry is considered for all the cases. C subroutines

are written to model various effects on deicing.

Keywords: de-icing, defrost, CFD, vehicle climate control

1 Introduction

The task of designing a windshield defroster is a difficult one. The defroster must adhere to

government regulations regarding the time to clear a minimum specified area of the

windshield. Previously the process involved design, then construction of a trial defroster,

followed by a testing program. Based on the test results, adjustments to the initial design

were made and the process repeated. CFD simulations have been used to augment the process

of defroster design [1], [3].

1 Lead Engineer, Fluent India Pvt. Ltd., Pune, [email protected]

2 Engineer Applications, Fluent India Pvt. Ltd., Pune, [email protected]

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2 CFD Modeling

Commercial CFD code FLUENT has been used in the present study. A simplified CFD

model is created and all the tests are performed over it. Since the model is non-real,

comparison of simulation results with actual experimental results are not available to

comment on the accuracy of simulation results. The paper tries to highlight the effect of

various parameters by showing only qualitative differences.

Geometry and Mesh

A simplified cabin geometry is considered for the present work. This is shown in Figure 1

below.

Figure 1: Simplified symmetric geometry with an inlet and outlet

The ice layer and the windshield are meshed with prism cells. A few layers of prism shaped

cells are grown inside the cabin, attached to the windshield to allow better flow development

as the air comes out of the defroster outlet (inlet of the domain). The remaining region is

meshed with uniform size tetrahedral cells. The mesh in the geometry is shown in Figure 2

below. On actual models, hexcore type mesh can be generated to reduce computational cost.

Figure 2: Mesh in the cabin geometry

inlet

symmetry

outlet

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Case Setup

FLUENT v6.3 is used in the present study. Turbulence in the flow-field is modeled using the

standard k-epsilon model. Enthalpy-porosity based solidification-melting model is used to

model the melting of ice [4]. The ice layer is modeled as an ice-water mixture with both

solidus and liquidus temperature specified. The standard CFD procedure for such a problem

is to first obtain a steady state constant temperature solution, then freeze the flow field and

solve for only energy in unsteady state.

3 Case Studies

This section covers studies done by varying different parameters that affect deicing patterns.

Effect of mesh near windshield The effect of mesh type near the windshield is considered here. Figure 3 shows the two

different types of mesh. A default mesh with only tetrahedral cells in the cabin is created.

Full tetrahedral mesh Boundary layered mesh

Figure 3: Mesh in the cabin geometry

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The second mesh has about 10 boundary layered cells grown from the windshield surface to

allow better flow development. Figure 4 shows the comparison of velocity contours on a

center-plane whereas Figure 5 shows the comparison of deicing rate.


Figure 4: Velocity field comparison


Figure 5: Comparison of deicing patterns

T = 600 s T = 600 s

T = 800 s T = 800 s

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The velocity field on the full-tetrahedral mesh is quite diffusive and is not very well attached

with the windshield whereas on the boundary layered mesh very well aligned with the

windshield and is also attached with it. This change is the flow-field translates into a big

difference when the deicing patterns are compared. Clearly, the rate of deicing is under-

predicted by the full-tetrahedral mesh. Therefore, resolving the near wall mesh properly is

very important to correctly predict the deicing patterns.

Effect of turbulence models

The effect of various turbulence modeling is studied in this section. The case (with prism

layered mesh inside the cabin grown from the windshield) was run with standard k-ω model

and standard k-є model. The k-ω turbulence model in Fluent is based on the Wilcox k-ω

model [4] and is found to be good for wall-bounded flows and free shear flows. Figure 6

shows the velocity field on the mid-plane obtained from the two simulations. The standard k-

ω model predicts a flow-filed that is very well attached to the windshield. This results in

faster de-icing of the windshield which is shown in Figure 7 below.

Standard k-omega model Standard k-epsilon model

Figure 6: Velocity field comparison

Effect of Melting of Ice

In the current de-icing formulation, ice layer is modeled as a semi-solid (mushy) zone with

constant values of the specific heat and thermal conductivity. These values correspond to the

ice material and remain as they are even when the ice starts melting and the value of liquid-

fraction is non-zero. In reality, as the ice-melts, the cells that represent partial-ice and partial-

water should have averaged properties of specific heat and thermal conductivity. This has

been incorporated using a small user defined function (UDF) that calculates averaged value

of specific heat and thermal conductivity based on the liquid fraction in a given cell.

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Standard k-omega Standard k-epsilon

Figure 7: Comparison of deicing patterns

So,

)1( ββ −×+×= icepliqpeffp CCC , where

β is the liquid fraction in the cell.

Similarly

)1( ββ −×+×= iceliqeff KKK

Since Cp cannot have a user-defined value in Fluent (due to stability concerns), the effect of

change in Cp is accounted for by an equivalent change in density. Figure 7 shows the effect of

accounting for variable K and Cp. The figures on left show the de-icing pattern based on

constant properties whereas those on the right show the pattern based on variable properties.

The melting process once started becomes slower in the case of variable property. This is

because the specific heat of water is more than that of ice and so the value of effective

specific heat is always greater than that of just ice once the melting starts. Higher value of

specific heat means slower transfer of heat and hence slower melting rate

T = 500 s T = 500 s

T = 700 s T = 700 s

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Effect of considering composite solids

Windshields in automobiles are made up of layers of glass and plastic (called laminates).

Some times the windshield glass is also tinted with plastic films to shade it. It is important to

consider the thermal effects of these plastic layers to correctly capture the de-icing profile.

One way is to actually model these plastic layers. Since the thickness of these laminates and

plastic films is so small that to actually resolve this small thickness, we may have to put a

very fine mesh in this small thickness, which may not be possible in real geometries.

Therefore the other alternative is to model just one solid for the entire windshield glass and

then use effective thermal conductivity and effective specific heat for this solid made up of

composite material. Again, since deriving an effective specific heat for composites is not so

straight forward, the other alternative which is available in FLUENT, is to model the thin

laminates as shell conduction walls [4]. Tables 1 and 2 show the material properties of

laminate plastic and the effective conductivity of the composite solid. Figure 8 shows the

glass-laminate windshield and the geometry when composite solid approach is used.

Contant K and Cp Variable K and Cp due to ice melting

Figure 7: Comparison of de-icing patterns

T = 600 s T = 600 s

T = 900 s T = 900 s

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Table 1: Properties of Plastic

Thermal conductivity 3.9 e-02 W/mK

Specific Heat 1900 J/kgK

Density 1370 kg/m3

Table 2: Properties of Glass Plastic composite

Layer Conductivity (W/mK) Thickness (In)

Glass 0.93 0.0044

Plastic 0.039 0.0005

Glass-plastic composite 0.2791 0.0049

Modeling plastic layer Modeling effective conductivity

Figure 8: Modeling glass-laminate composite

Figure 9 shows the comparison between the two approaches shown in Figure 8. It is clear

from this figure that the deicing is fast in the case where the glass-laminate is modeled as a

composite. This is because the effective specific is not considered in this case and only the

specific heat of glass is used as the effective specific heat. Since the specific heat of glass is

lesser than that of plastic, deicing is faster.

In the second case, instead of modeling the glass-laminate as composite, the laminate is

modeled as a wall with shell conduction effects. The comparison of deicing from this

simulation with the baseline simulation (where the laminate is modeled as a separate solid) is

shown in Figure 10 below. The rate of deicing in both the simulations is almost the same.

This confirms that modeling the laminate solid zone as a shell-conduction wall is a good

alternative to modeling the laminate as a separate solid zone.

Ice layer

Plastic layer

Glass

layer

Ice layer

Plastic and glass

layer merged to

create one solid

zone

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Modeling plastic layer Modeling using Keff of the composite


4 Conclusion

This paper summarizes some best-practice techniques involved in the CFD simulation of

windshield deicing. There are several key factors like mesh size, turbulence models, variable

material properties etc. which need to be taken into account while modeling windshield

deicing. Case studies on each of these factors are presented along with a plausible

explanation of the effect of these factors. Although FLUENT v6.3 is used to demonstrate the

effect of various factors, it must be emphasized here that these factors are physics dependent

and are not specific to any particular CFD code. With regulation norms becoming more and

more stringent, it is all the more important to achieve accuracy in predicting deicing patterns.

This paper can be used as a guide to accurately predict windshield deicing patterns using

CFD.

T = 700 s T = 700 s

T = 900 s T = 900 s

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Modeling plastic layer Modeling using shell-conduction


References

[1] A. Farag and L. J. Huang CFD Analysis and Validation of Automotive Windshield De-

Icing Simulation. SAE Technical Paper Series. 2003-01-1079, 2003

[2] S. Roy, H. Kumar, R. Anderson Efficient defrosting of an inclined flat surface. Int.

Journal of Heat and Mass Transfer. 48: 2613-2624, 2005

[3] Z. Tastan and M. Matthes Windshield Deicing of a passenger car, Fluent Gernany

Automotive User Group Meeting, 2002

[4] Fluent 6.3 Users Guide, Ansys Inc., Cannosburg PA, 2006

T = 700 s T = 700 s

T = 900 s T = 900 s

case-studies on cfd simulation of windshield de-icing

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